Abstract:

With the rapid development of network and information technology, a large number of electronic documents appear on the network, and the similarity computaion between the documents is an important means of document processing. For large-scale collection of documents, vector space model (VSM) is usually used for document representation, but the method is facing the problems of higher dimension and lack of semantic similarity. An improved method for calculating the similarity of document is proposed. Metadata feature vectors are selected from a large number of representative feature space, so that it can reduce the dimension of the vector space. The domain concept tree is constructed and the algorithm for computing document similarity is designed. In order to improve the document semantic similarity of algorithm performance, the synonym concepts which exist in widespread areas are processed. The experimental results show that the proposed method can improve the performance of document similarity computation based on the dimensionality reduction and the concepts similarity computing.